An improved hyperspectral sensing approach for the rapid determination of copper ion concentrations in water environment using short-wavelength infrared spectroscopy

Copper ion is one of the hazardous pollutants often present in industrial wastewater or acid mine drainage that is regarded as a primary environmental challenge. Hyperspectral remote sensing has a long tradition in water quality monitoring. However, its application in heavy metal detection is relatively similar, and the detection is highly influenced by water turbidity or total suspended matter (TSM), requiring research efforts to improve accuracy and generalize the applicability of this technique. In this study, the use of simple filtration (pore size of 0.7 mu m) for sample pretreatment to improve hyperspectral remote sensing of copper ion concentrations (Cu, 100-1000 mg/L) in water samples is proposed. A wide variety of water samples, including as-prepared and field (fish pond and river water) samples, were investigated to validate the developed method. Spectral data containing sensitive bands characterized in the range of 900-1100 nm were first preprocessed with logarithm transformation, followed by quantitative prediction model development using stepwise multivariate linear regression (SMLR) with the most sensitive wavebands at around 900 nm and 1080 nm. Satisfactory prediction performance for Cu ions was found for turbid water samples (TSM greater than approximately 200 mg/L) after simple filtration pretreatment, suggesting that pretreatment removed suspended solids in the mixtures and enhanced the spectral features of Cu ions in the model. Moreover, good agreement between the laboratory results and the field samples (adjusted R-2 > 0.95 and NRMSE <0.15) highlights the suitability of the developed model and filtration pretreatment for obtaining relevant information for the rapid determination of Cu ion concentrations in complex water samples.